search for: lda_r

I'm trying to classify some observations using lda and I'm getting a strange error. I loaded the MASS package and created a model like so: >train <- mod1[mod1$rand < 1.7,] >classify <- mod1[mod1$rand >= 1.7,] >lda_res <- lda(over_win ~ t1_scrd_a + t1_alwd_a, data=train, CV=TRUE) That works, and all is well until I try to do a prediction for the holdouts: >lda_pred <- predict(lda_res, classify)$class Error in predict(lda_res, classify) : no applicable method for "predict" If I try predict....

...roblems shows collinearity for p > n-2. Does anyone know why this is the case? Does lda (MASS) use a different algorithm? #Question2: #========= When I plot leave-one-out CV based on lda (averaged over 500 simulated data sets), I see a pick (see the link http://homepages.ed.ac.uk/mkhondok/temp/lda_R-help-CV.png ) at p = n-3 (not n-2!). I would appreciate if someone could help me get an explanation for this behaviour. ## R code ## Reproducible example library(MASS) # n: sample size # p: number of variables ## Function ## -------- test.fun<-function(n,p){ x<-matrix (rnorm(n*p), ncol=p...